JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3 Issue : 7 Date : July 2008
Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach
Vishwas Puttige and Sreenatha Anavatti
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In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple
neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonlinear system.
Models for such MIMO system are expected to be adaptive to dynamic behaviour and robust to
environmental variations. This task of accurate modelling has been achieved with a multi-network
architecture. The multi-network with dynamic selection technique allows a combination of online
and offline neural network models to be used in the architecture where the most suitable outputs
are selected based on a given criterion. The neural network models are based on the
autoregressive technique. The online network uses a novel training scheme with memory retention.
Flight test validation results for online and offline models are presented. The multi-network dynamic
selection technique has been validated on real-time hardware in the loop (HIL) simulation and the
results show the superiority in performance compared to the individual models.